Introduction¶

Companies invest significant resources in marketing campaigns to convert prospective customers into current ones. This process is essential for maintaining revenue levels, reinvesting to improve service quality, responding to shareholders, and more. Therefore, understanding why customers leave and predicting churn with enough anticipation to implement a retention plan is an integral part of business operations. With this in mind, this kernel will focus on exploring the available data to identify potential causes of customer churn, laying the groundwork for future machine learning models.

Instead of using Matplotlib or Seaborn, I chose Plotly for this kernel due to its excellent interactivity. Additionally, Plotly, when combined with Dash, provides tools to build web applications with interactive dashboards.

I hope you find this kernel helpful. If you have any questions or suggestions for improvement, please feel free to open an issue or start a discussion.

Requirements¶

Pandas version: 1.5.3
NumPy version: 1.24.4
SciPy version: 1.10.1
Matplotlib version: 3.7.0
Missingno version: 0.5.2
Plotly version: 5.13.1

Import Libraries libraries¶

Utility functions¶

resumetable() | expanded_describe():¶

spot_missing_values():¶

reshape_df()¶

custom_bar_chart()¶

custome_histograms()¶

costum_boxplot()¶

custom_line_chart()¶

serv_dist_in_num_servs()¶

Understanding the data¶

Loading dataset¶

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

Quick view to the dataset.¶

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
5398	3936-QQFLL	Male	0	No	No	2	Yes	No	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	19.75	39.3	No
1023	7460-ITWWP	Female	1	Yes	No	45	Yes	No	Fiber optic	No	No	Yes	No	No	Yes	Month-to-month	Yes	Electronic check	86.10	3861.45	No
3080	1777-JYQPJ	Male	0	No	No	2	No	No phone service	DSL	No	No	No	No	No	No	Month-to-month	No	Mailed check	24.30	38.45	No

General Info about Dataset:¶

More info could be found in the following link:

https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/11/telco-customer-churn-1113

Column description¶

Demographic info:¶

gender
SeniorCitizen: Indicates if the customer is under 65 or older: Yes, No
Partner
Dependents

Company services:¶

PhoneService,
Multiple lines,
Internet service,
Online security,
Online backup,
Device protection,
Tech support,
Streaming TV,
Streaming movies

Customer account information:¶

CustomerID
Contract
Ternure
Paperless billing
Payment method
Monthly charges
Total charges

Additional considerations:¶

Column tenure indicates the months that a particular customer has been with the company by the end of the quarter (The fiscal quarter that the data has been derived from (e.g. Q3)). I will assume that all the customer cancelation dates are the same

	column_name	dtypes	unique2	count	top2	freq2	mean	std	min	25%	50%	75%	max	First Value	Second Value	Third Value	Entropy
0	customerID	object	7043	7043.0	0002-ORFBO	1	-	-	-	-	-	-	-	7590-VHVEG	5575-GNVDE	3668-QPYBK	3.85
1	gender	object	2	7043.0	Male	3555	-	-	-	-	-	-	-	Female	Male	Male	0.30
2	SeniorCitizen	int64	2	7043.0	0.0	5901	0.162147	0.368612	0.0	0.0	0.0	0.0	1.0	0	0	0	0.19
3	Partner	object	2	7043.0	No	3641	-	-	-	-	-	-	-	Yes	No	No	0.30
4	Dependents	object	2	7043.0	No	4933	-	-	-	-	-	-	-	No	No	No	0.27
5	tenure	int64	73	7043.0	1.0	613	32.371149	24.559481	0.0	9.0	29.0	55.0	72.0	1	34	2	1.78
6	PhoneService	object	2	7043.0	Yes	6361	-	-	-	-	-	-	-	No	Yes	Yes	0.14
7	MultipleLines	object	3	7043.0	No	3390	-	-	-	-	-	-	-	No phone service	No	No	0.41
8	InternetService	object	3	7043.0	Fiber optic	3096	-	-	-	-	-	-	-	DSL	DSL	DSL	0.46
9	OnlineSecurity	object	3	7043.0	No	3498	-	-	-	-	-	-	-	No	Yes	Yes	0.45
10	OnlineBackup	object	3	7043.0	No	3088	-	-	-	-	-	-	-	Yes	No	Yes	0.46
11	DeviceProtection	object	3	7043.0	No	3095	-	-	-	-	-	-	-	No	Yes	No	0.46
12	TechSupport	object	3	7043.0	No	3473	-	-	-	-	-	-	-	No	No	No	0.45
13	StreamingTV	object	3	7043.0	No	2810	-	-	-	-	-	-	-	No	No	No	0.46
14	StreamingMovies	object	3	7043.0	No	2785	-	-	-	-	-	-	-	No	No	No	0.46
15	Contract	object	3	7043.0	Month-to-month	3875	-	-	-	-	-	-	-	Month-to-month	One year	Month-to-month	0.43
16	PaperlessBilling	object	2	7043.0	Yes	4171	-	-	-	-	-	-	-	Yes	No	Yes	0.29
17	PaymentMethod	object	4	7043.0	Electronic check	2365	-	-	-	-	-	-	-	Electronic check	Mailed check	Mailed check	0.59
18	MonthlyCharges	float64	1585	7043.0	20.05	61	64.761692	30.090047	18.25	35.5	70.35	89.85	118.75	29.85	56.95	53.85	3.02
19	TotalCharges	object	6531	7043.0		11	-	-	-	-	-	-	-	29.85	1889.5	108.15	3.80
20	Churn	object	2	7043.0	No	5174	-	-	-	-	-	-	-	No	No	Yes	0.25

Insights:¶

customerID is unique, which means that we have data from 7043 different customers.
TotalCharges, most of its values are unique. It has a wrong data types, and the top frequent value appear empty (' ').
Our dataset has 21 columns and 7043 rows.

Spotting missing values¶

	column_name	dtypes	value: ' '	total_missing	percent_missing
0	customerID	object	0.0	0.0	0.00%
1	gender	object	0.0	0.0	0.00%
2	SeniorCitizen	int64	0.0	0.0	0.00%
3	Partner	object	0.0	0.0	0.00%
4	Dependents	object	0.0	0.0	0.00%
5	tenure	int64	0.0	0.0	0.00%
6	PhoneService	object	0.0	0.0	0.00%
7	MultipleLines	object	0.0	0.0	0.00%
8	InternetService	object	0.0	0.0	0.00%
9	OnlineSecurity	object	0.0	0.0	0.00%
10	OnlineBackup	object	0.0	0.0	0.00%
11	DeviceProtection	object	0.0	0.0	0.00%
12	TechSupport	object	0.0	0.0	0.00%
13	StreamingTV	object	0.0	0.0	0.00%
14	StreamingMovies	object	0.0	0.0	0.00%
15	Contract	object	0.0	0.0	0.00%
16	PaperlessBilling	object	0.0	0.0	0.00%
17	PaymentMethod	object	0.0	0.0	0.00%
18	MonthlyCharges	float64	0.0	0.0	0.00%
19	TotalCharges	object	11.0	11.0	0.16%
20	Churn	object	0.0	0.0	0.00%

Insights:¶

Even though there is not missing values such as np.nan, there are strings values such as ' ' that indicate missing values.
According to the previous table, the only column with missing values is TotalCharges. Since it represents 0.16% of the data, the plan of action will be to drop those 11 rows.
customerID could be dropped. At this stage, it seems that the id code nomenclature does not hold any hidden information.

	column_name	dtypes	percent_missing
0	gender	object	0.00%
1	SeniorCitizen	int64	0.00%
2	Partner	object	0.00%
3	Dependents	object	0.00%
4	tenure	int64	0.00%
5	PhoneService	object	0.00%
6	MultipleLines	object	0.00%
7	InternetService	object	0.00%
8	OnlineSecurity	object	0.00%
9	OnlineBackup	object	0.00%
10	DeviceProtection	object	0.00%
11	TechSupport	object	0.00%
12	StreamingTV	object	0.00%
13	StreamingMovies	object	0.00%
14	Contract	object	0.00%
15	PaperlessBilling	object	0.00%
16	PaymentMethod	object	0.00%
17	MonthlyCharges	float64	0.00%
18	TotalCharges	float64	0.00%
19	Churn	object	0.00%

Insights¶

Previous tables shows that there is not missing values. It possible to proceed with data visualization.

Data Visualization¶

Insigths¶

The dependent variable is Churn. In general, 26.6% of customer churn, while the company was able to retain 73.4% of the customers.
Since the objective is to predict customer churn, this dataset is unbalanced.

VIZ - Demographic evaluation¶

Insights¶

Sex distribution among male and female are similar. It is not showing a clear difference between churn and retained customers.
Curiously, customers over 65 years old are more likely to churn (around 42% of churn rate). It could be related to age issues.
In general, the number of customers with or without a partner is very similar. On the other hand, most customers do not have dependents (around 70% of them). The interesting point is that customers without a partner or dependents are more likely to churn (approximately 33% and 31%, respectively)

VIZ - Service evaluation¶

Insights¶

Even though most customers have phone service (around 90%), customer with or without service have a similar rate churn (around 25% to 27%).
Customers with multiline phone services are slightly more likely to churn, around 29%, compared to the 25% churn rate for customers without multiline services.
Around 78% of customer has internet service (fiber optic and DSL). Interestingly, customers with Fiber optic service are more likely to churn, with approximately a churn rate of 42%.
Figures show that customers who do not complement their internet service with online security, online backup, device protection, or tech support are more likely to churn. The churn rate for those customers ranges between 39% to 42%. Additionally, the churn rate for customers without or with services such as streaming tv and streaming movies are similar, which is between 30% to 34% churn.

VIZ - Customer account information¶

Insights¶

Turning to customer service information, 55% of customers have a month-to-month contract with a high churn rate of approximately 43%, which is relatively high compared to 11% churn of customers with one-year contracts or the roughly 3% churn for customers with two-year contracts.
Including tenure in the analysis or contract and churn, the histogram shows that the greater the customer commitment, the later the customer service cancelation is. Around 62% of the month-to-month customers churn within the first year. In contrast to month-to-month customers, customers with one-year or two-year contracts seem to delay their churn. Around 70% of One-year contract customers cancel their service after three years, and 65% of two-year contract customers cancel their service after five years.
In general, comparing the churn rate per month, we can see that the more time the client is in the company, the lower the customer attrition will be.
Interestingly, 59% of customers prefer paperless billing; however, in terms of churn, paperless billing represents a 33% churn compared to other forms of billing that represent 16% churn.
Slightly over a third of the customers use electronic checks as preferred payment methods; however, it has a 45% churn. Other payment methods (such as mailed checks, automatic payment with bank transfer, and credit cards) present a churn rate of around 15% to 19%.
Around 62% of customers cancel their services the first month, representing 380 customers. After the first three months, the churn rate changes to be lower than the retention rate of customers, and for each month that the company keeps the client, the company can reduce the churn rate up to around 2% (customer with six years).
Additionally, according to the boxplot, it seems that customers with higher monthly charges are more likely to churn than those who have to pay lower monthly bills. This difference is accentuated for those customers with a longer time in the company.
As was expected, the total charges per customer increased as the customer tenure increased. However, it is interesting that customers with higher total charges are more likely to churn than customers with lower total charges.
Counting multiline as a service, each customer can add up to nine services. According to the bar chart, with around a 45% churn rate, customers with three services are more likely to churn.
If we give a deeper look at the service distribution of active services for customers who have selected 3 or 4 services, we can see that the churn rate for customers with Internet service (Fiber optic) reaches up to 59% and 51%, respectively.

Note: For the following steps (data mining models), I will keep the new column 'num_services'.

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